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Comparison of Cognitive-Behavioral, Relaxation, and Self-Modeling Interventions for Depression Among Middle-School Students

Kahn, James S., Kehle, Thomas J., Jenson, William R., Clark, Elaine·1990·School Psychology Review

Abstract

The efficacy of short-term cognitive-behavioral therapy, relaxation training, and self-modeling interventions for the treatment of depression among middle school students was investigated. Sixty-eight moderate to severely “experimentally diagnosed” latency age and depressed early adolescent subjects (ages 10–14) were assigned randomly to one of three active treatments or waitlist-control. Self-report and interview measures of depression and self-esteem were used to assess treatment effects. Results indicated that all treatment conditions, relative to the waitlist-control, evidenced a significant decrease in depression and an increase in self-esteem. Parent-report data, as well as data from treated control subjects, provided further support for the potential effectiveness of the short-term structured school-based interventions.

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Metadata

Authors
Kahn, J. S., Kehle, T. J., Jenson, W. R., & Clark, E.
Venue
School Psychology Review
Published
1990-06-01
Volume
19
Issue
2
Pages
196-211
ISSN
  • 0279-6015 (ISSN-L)
  • 2372-966X
OpenAlex
W2725927370
Cited by
288
References
0
FWCI
5.13
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Comparison of Cognitive-Behavioral, Relaxation, and Self-Modeling Interventions for Depression Among Middle-School Students. https://app.cochat.ai/papers/10.1080/02796015.1990.12085457
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Kahn, J. S., Kehle, T. J., Jenson, W. R., & Clark, E. (1990). Comparison of Cognitive-Behavioral, Relaxation, and Self-Modeling Interventions for Depression Among Middle-School Students. School Psychology Review. https://doi.org/10.1080/02796015.1990.12085457

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.